When would online platforms pay data dividends

TL;DR

This paper models the strategic decision-making of online platforms and users regarding data dividends, privacy valuation, and data protection investments using a game-theoretic approach to inform policy discussions.

Contribution

It introduces a principal-agent Stackelberg game model to analyze when platforms would pay data dividends and how privacy valuation influences their decisions.

Findings

Platforms are more likely to pay data dividends when user privacy valuation is low.

Data protection investments depend on the trade-off between privacy concerns and service quality.

The model provides insights for policymakers on the implications of mandating data dividends.

Abstract

Online platforms, including social media and search platforms, have routinely used their users' data for targeted ads, to improve their services, and to sell to third-party buyers. But an increasing awareness of the importance of users' data privacy has led to new laws that regulate data-sharing by platforms. Further, there have been political discussions on introducing data dividends, that is paying users for their data. Three interesting questions are then: When would these online platforms be incentivized to pay data dividends? How does their decision depend on whether users value their privacy more than the platform's free services? And should platforms invest in protecting users' data? This paper considers various factors affecting the users' and platform's decisions through utility functions. We construct a principal-agent model using a Stackelberg game to calculate their optimal decisions and qualitatively discuss the implications. Our results could inform a policymaker trying to understand the consequences of mandating data dividends.